SoroushVahidi/scidocs huggingface dataset:
Table-top spatially-augmented realty: bringing physical models to life with projected imagery
(1999)

Despite the availability of high-quality graphics systems, architects and designers still build scaled physical models of buildings and products. These physical models have many advantages, however they are typically static in structure and surface characteristics. They are inherently lifeless. In contrast, high-quality graphics systems are tremendously flexible, allowing viewers to see alternative structures, facades, textures, cut-away views, and even dynamic effects such as changing lighting, moving automobiles, people, etc. We introduce a combination of these approaches that builds on our previously-published projector-based Spatially-Augmented Reality techniques. The basic idea is to aim multiple ceiling-mounted light projectors inward to graphically augment table-top scaled physical models of buildings or products. This approach promises to provide very compelling hybrid visualizations that afford the benefits of both traditional physical models, and modern computer graphics, effectively "bringing to life" table-top
Visual Event Summarization on Social Media using Topic Modelling and Graph-based Ranking Algorithms
(2015)

Due to the increasing popularity of microblogging platforms, the amount of messages (posts) related to public events, especially posts encompassing multimedia content, is steadily increasing. The inclusion of images can convey much more information about the event, compared to their text, which is typically very short (e.g., tweets). Although such messages can be quite informative regarding different aspects of the event, there is a lot of spam and redundancy making it challenging to extract pertinent insights. In this work, we describe a summarization framework that, given a set of social media messages about an event, aims to select a subset of images derived from them, that, at the same time, maximizes the relevance of the selected images and minimizes their redundancy. To this end, we propose a topic modeling technique to capture the relevance of messages to event topics and a graph-based algorithm to produce a diverse ranking of the selected high-relevance images. A user-centered evaluation on a large Twitter dataset around several real-world events demonstrates that the proposed method considerably outperforms a number of state-of-the-art summarization algorithms in terms of result relevance, while at the same time it is also highly competitive in terms of diversity. Namely, we get an improvement of 25% in terms of precision compared to the second best result, and 7% in terms of diversity.
Calibration of RGB camera with velodyne LiDAR
(2014)

Calibration of the LiDAR sensor with RGB camera finds its usage in many application fields from enhancing image classification to the environment perception and mapping. This paper presents a pipeline for mutual pose and orientation estimation of the mentioned sensors using a coarse to fine approach. Previously published methods use multiple views of a known chessboard marker for computing the calibration parameters, or they are limited to the calibration of the sensors with a small mutual displacement only. Our approach presents a novel 3D marker for coarse calibration which can be robustly detected in both the camera image and the LiDAR scan. It also requires only a single pair of camera-LiDAR frames for estimating large sensors displacement. Consequent refinement step searches for more accurate calibration in small subspace of calibration parameters. The paper also presents a novel way for evaluation of the calibration precision using projection error.
Sketch2normal: deep networks for normal map generation
(2017)

Normal maps are of great importance for many 2D graphics applications such as surface editing, re-lighting, texture mapping and 2D shading etc. Automatically inferring normal map is highly desirable for graphics designers. Many researchers have investigated the inference of normal map from intuitive and flexiable line drawing based on traditional geometric methods while our proposed deep networks-based method shows more robustness and provides more plausible results.
Delayed enhancement of multitasking performance: Effects of anodal transcranial direct current stimulation on the prefrontal cortex
(2015)

BACKGROUND
The dorsolateral prefrontal cortex (DLPFC) has been proposed to play an important role in neural processes that underlie multitasking performance. However, this claim is underexplored in terms of direct causal evidence.


OBJECTIVE
The current study aimed to delineate the causal involvement of the DLPFC during multitasking by modulating neural activity with transcranial direct current stimulation (tDCS) prior to engagement in a demanding multitasking paradigm.


METHODS
The study is a single-blind, crossover, sham-controlled experiment. Anodal tDCS or sham tDCS was applied over left DLPFC in forty-one healthy young adults (aged 18-35 years) immediately before they engaged in a 3-D video game designed to assess multitasking performance. Participants were separated into three subgroups: real-sham (i.e., real tDCS in the first session, followed by sham tDCS in the second session 1 h later), sham-real (sham tDCS first session, real tDCS second session), and sham-sham (sham tDCS in both sessions).


RESULTS
The real-sham group showed enhanced multitasking performance and decreased multitasking cost during the second session, compared to first session, suggesting delayed cognitive benefits of tDCS. Interestingly, performance benefits were observed only for multitasking and not on a single-task version of the game. No significant changes were found between the first and second sessions for either the sham-real or the sham-sham groups.


CONCLUSIONS
These results suggest a causal role of left prefrontal cortex in facilitating the simultaneous performance of more than one task, or multitasking. Moreover, these findings reveal that anodal tDCS may have delayed benefits that reflect an enhanced rate of learning.
Binarization-free OCR for historical documents using LSTM networks
(2015)

A primary preprocessing block of almost any typical OCR system is binarization, through which it is intended to remove unwanted part of the input image, and only keep a binarized and cleaned-up version for further processing. The binarization step does not, however, always perform perfectly, and it can happen that binarization artifacts result in important information loss, by for instance breaking or deforming character shapes. In historical documents, due to a more dominant presence of noise and other sources of degradations, the performance of binarization methods usually deteriorates; as a result the performance of the recognition pipeline is hindered by such preprocessing phases. In this paper, we propose to skip the binarization step by directly training a 1D Long Short Term Memory (LSTM) network on gray-level text lines. We collect a large set of historical Fraktur documents, from publicly available online sources, and form train and test sets for performing experiments on both gray-level and binarized text lines. In order to observe the impact of resolution, the experiments are carried out on two identical sets of low and high resolutions. Overall, using gray-level text lines, the 1D LSTM network can reach 24% and 19% lower error rates on the low- and high-resolution sets, respectively, compared to the case of using binarization in the recognition pipeline.
Joint effects of emotion and color on memory.
(2013)

Numerous studies have shown that memory is enhanced for emotionally negative and positive information relative to neutral information. We examined whether emotion-induced memory enhancement is influenced by low-level perceptual attributes such as color. Because in everyday life red is often used as a warning signal, whereas green signals security, we hypothesized that red might enhance memory for negative information and green memory for positive information. To capture the signaling function of colors, we measured memory for words standing out from the context by color, and manipulated the color and emotional significance of the outstanding words. Making words outstanding by color strongly enhanced memory, replicating the well-known von Restorff effect. Furthermore, memory for colored words was further increased by emotional significance, replicating the memory-enhancing effect of emotion. Most intriguingly, the effects of emotion on memory additionally depended on color type. Red strongly increased memory for negative words, whereas green strongly increased memory for positive words. These findings provide the first evidence that emotion-induced memory enhancement is influenced by color and demonstrate that different colors can have different functions in human memory.
Design of a Real-Time Micro-Winch Controller for Kite-Power Applications Master ’ s Thesis in Embedded
(2015)

Airborne wind energy is a technology to extract energy from high altitude winds. This technology is under heavy development by several companies and universities. An actual problem with the commercialization of the technology is the reliability and safety of the system. In this thesis a real time environment suitable to perform research and further development of the prototype steering and depower control is proposed. Additionally, the overload prevention of the kite lines is researched. This thesis presents a method to estimate the tension on the kite control tapes using only one tension sensor. Thus, reducing the amount of hardware needed to protect the kite from overloads. The method relies on the characterization of the powertrain efficiency and can be used to estimate the tensions at high loads. An algorithm to limit the forces on the steering lines by depowering the kite is shown; it controls the depower state of the kite based on the desired depower state, the actual tension, and previous tensions on the KCU’s tapes. The tensions history is used to calculate a higher depower state to prevent future overloads, this reduces the amount of action needed by the motors and enable the system to use a brake to save energy. The limiter output is used as an input to a position controller, which allows the project to use off the shelf solutions to build the KCU prototype. The controller was implemented in a real time system and is able to run as fast as 20 Hz being the communication protocol the execution time bottleneck. The control algorithms were tested using a mathematical model of the kite, the environment, and trajectory control inputs from FreeKiteSim. Three scenarios were considered for the model test, normal operation, overload operation without tension limitation, and overload operation with tension limitation. The apparent wind speed during the reel out phase of the normal scenario is approximately 30 m/s and 35 m/s for the overload scenarios. During the overload scenario the limiter spent roughly 22% more energy than the normal operation scenario to counteract an increase of 5 m/s in the apparent wind during 3.5 hours of operation, but it spent 15% less energy than the overload scenario without tension limitation.
Analyzing News Media Coverage to Acquire and Structure Tourism Knowledge
(2008)

Destination image significantly influences a tourist’s decision-making process. The impact of news media coverage on destination image has attracted research attention and became particularly evident after catastrophic events such as the 2004 Indian Ocean earthquake that triggered a series of lethal tsunamis. Building upon previous research, this article analyzes the prevalence of tourism destinations among 162 international media sites. Term frequency captures the attention a destination receives—from a general and, after contextual filtering, from a tourism perspective. Calculating sentiment estimates positive and negative media influences on destination image at a given point in time. Identifying semantic associations with the names of countries and major cities, the results of co-occurrence analysis reveal the public profiles of destinations, and the impact of current events on media coverage. These results allow national tourism organizations to assess how their destination is covered by news media in general, and in a specific tourism context. To guide analysts and marketers in this assessment, an iterative analysis of semantic associations extracts tourism knowledge automatically, and represents this knowledge as ontological structures.
Composite Connectors for Composing Software Components
(2007)

In a component-based system, connectors are used to compose components. Connectors should have a semantics that makes them simple to construct and use. At the same time, their semantics should be rich enough to endow them with desirable properties such as genericity, compositionality and reusability. For connector construction, compositionality would be particularly useful, since it would facilitate systematic construction. In this paper we describe a hierarchical approach to connector definition and construction that allows connectors to be defined and constructed from sub-connectors. These composite connectors are indeed generic, compositional and reusable. They behave like design patterns, and provide powerful composition connectors.
Measured Gene-Environment Interactions in Psychopathology: Concepts, Research Strategies, and Implications for Research, Intervention, and Public Understanding of Genetics.
(2006)

There is much curiosity about interactions between genes and environmental risk factors for psychopathology, but this interest is accompanied by uncertainty. This article aims to address this uncertainty. First, we explain what is and is not meant by gene-environment interaction. Second, we discuss reasons why such interactions were thought to be rare in psychopathology, and argue instead that they ought to be common. Third, we summarize emerging evidence about gene-environment interactions in mental disorders. Fourth, we argue that research on gene-environment interactions should be hypothesis driven, and we put forward strategies to guide future studies. Fifth, we describe potential benefits of studying measured gene-environment interactions for basic neuroscience, gene hunting, intervention, and public understanding of genetics. We suggest that information about nurture might be harnessed to make new discoveries about the nature of psychopathology.
The Economics of State Fragmentation-Assessing the Economic Impact of Secession
(2018)

This paper provides empirical evidence that declaring independence significantly lowers per capita GDP based on a large panel of countries covering the period 1950-2016. To do so, we rely on a semi-parametric identification strategy that controls for the confounding effects of past GDP dynamics, anticipation effects, unobserved heterogeneity, model uncertainty and effect heterogeneity. Our baseline results indicate that declaring independence reduces per capita GDP by around 20% in the long run. We subsequently propose a quadruple-difference procedure to demonstrate that the results are not driven by simulation and matching inaccuracies or spillover effects. A second methodological novelty consists of the development of a two-step estimator that relies on the control function approach to control for the potential endogeneity of the estimated independence payoffs and their potential determinants, to shed some light on the primary channels driving our results. We find tentative evidence that the adverse effects of independence decrease in territorial size, pointing to the presence of economies of scale, but that they are mitigated when newly independent states liberalize their trade regime or use their new-found political autonomy to democratize.
AlterEgo: A Personalized Wearable Silent Speech Interface
(2018)

We present a wearable interface that allows a user to silently converse with a computing device without any voice or any discernible movements - thereby enabling the user to communicate with devices, AI assistants, applications or other people in a silent, concealed and seamless manner. A user's intention to speak and internal speech is characterized by neuromuscular signals in internal speech articulators that are captured by the AlterEgo system to reconstruct this speech. We use this to facilitate a natural language user interface, where users can silently communicate in natural language and receive aural output (e.g - bone conduction headphones), thereby enabling a discreet, bi-directional interface with a computing device, and providing a seamless form of intelligence augmentation. The paper describes the architecture, design, implementation and operation of the entire system. We demonstrate robustness of the system through user studies and report 92% median word accuracy levels.
Be Quiet? Evaluating Proactive and Reactive User Interface Assistants
(2003)

This research examined the ability of an anthropomorphic interface assistant to help people learn and use an unfamiliar text-editing tool, with a specific focus on assessing proactive assistant behavior. Participants in the study were introduced to a text editing system that used keypress combinations for invoking the different editing operations. Participants then were directed to make a set of prescribed changes to a document with the aid either of a paper manual, an interface assistant that would hear and respond to questions orally, or an assistant that responded to questions and additionally made proactive suggestions. Anecdotal evidence suggested that proactive assistant behavior would not enhance performance and would be viewed as intrusive. Our results showed that all three conditions performed similarly on objective editing performance (completion time, commands issued, and command recall), while the participants in the latter two conditions strongly felt that the assistant’s help was valuable.
Calibrating Noise to Sensitivity in Private Data Analysis
(2006)

We continue a line of research initiated in [10, 11] on privacypreserving statistical databases. Consider a trusted server that holds a database of sensitive information. Given a query function f mapping databases to reals, the so-called true answer is the result of applying f to the database. To protect privacy, the true answer is perturbed by the addition of random noise generated according to a carefully chosen distribution, and this response, the true answer plus noise, is returned to the user. Previous work focused on the case of noisy sums, in which f = P i g(xi), where xi denotes the ith row of the database and g maps database rows to [0, 1]. We extend the study to general functions f , proving that privacy can be preserved by calibrating the standard deviation of the noise according to the sensitivity of the function f . Roughly speaking, this is the amount that any single argument to f can change its output. The new analysis shows that for several particular applications substantially less noise is needed than was previously understood to be the case. The first step is a very clean characterization of privacy in terms of indistinguishability of transcripts. Additionally, we obtain separation results showing the increased value of interactive sanitization mechanisms over non-interactive.
Scaling personalized web search
(2003)

Recent web search techniques augment traditional text matching with a global notion of "importance" based on the linkage structure of the web, such as in Google's PageRank algorithm. For more refined searches, this global notion of importance can be specialized to create personalized views of importance--for example, importance scores can be biased according to a user-specified set of initially-interesting pages. Computing and storing all possible personalized views in advance is impractical, as is computing personalized views at query time, since the computation of each view requires an iterative computation over the web graph. We present new graph-theoretical results, and a new technique based on these results, that encode personalized views as partial vectors. Partial vectors are shared across multiple personalized views, and their computation and storage costs scale well with the number of views. Our approach enables incremental computation, so that the construction of personalized views from partial vectors is practical at query time. We present efficient dynamic programming algorithms for computing partial vectors, an algorithm for constructing personalized views from partial vectors, and experimental results demonstrating the effectiveness and scalability of our techniques.
Learning Fair Representations
(2013)

We propose a learning algorithm for fair classification that achieves both group fairness (the proportion of members in a protected group receiving positive classification is identical to the proportion in the population as a whole), and individual fairness (similar individuals should be treated similarly). We formulate fairness as an optimization problem of finding a good representation of the data with two competing goals: to encode the data as well as possible, while simultaneously obfuscating any information about membership in the protected group. We show positive results of our algorithm relative to other known techniques, on three datasets. Moreover, we demonstrate several advantages to our approach. First, our intermediate representation can be used for other classification tasks (i.e., transfer learning is possible); secondly, we take a step toward learning a distance metric which can find important dimensions of the data for classification.
Three naive Bayes approaches for discrimination-free classification
(2010)

In this paper, we investigate how to modify the naive Bayes classifier in order to perform classification that is restricted to be independent with respect to a given sensitive attribute. Such independency restrictions occur naturally when the decision process leading to the labels in the data-set was biased; e.g., due to gender or racial discrimination. This setting is motivated by many cases in which there exist laws that disallow a decision that is partly based on discrimination. Naive application of machine learning techniques would result in huge fines for companies. We present three approaches for making the naive Bayes classifier discrimination-free: (i) modifying the probability of the decision being positive, (ii) training one model for every sensitive attribute value and balancing them, and (iii) adding a latent variable to the Bayesian model that represents the unbiased label and optimizing the model parameters for likelihood using expectation maximization. We present experiments for the three approaches on both artificial and real-life data.
Using Visual Text Mining to Support the Study Selection Activity in Systematic Literature Reviews
(2011)

Background: A systematic literature review (SLR) is a methodology used to aggregate all relevant existing evidence to answer a research question of interest. Although crucial, the process used to select primary studies can be arduous, time consuming, and must often be conducted manually. Objective: We propose a novel approach, known as 'Systematic Literature Review based on Visual Text Mining' or simply SLR-VTM, to support the primary study selection activity using visual text mining (VTM) techniques. Method: We conducted a case study to compare the performance and effectiveness of four doctoral students in selecting primary studies manually and using the SLR-VTM approach. To enable the comparison, we also developed a VTM tool that implemented our approach. We hypothesized that students using SLR-VTM would present improved selection performance and effectiveness. Results: Our results show that incorporating VTM in the SLR study selection activity reduced the time spent in this activity and also increased the number of studies correctly included. Conclusions: Our pilot case study presents promising results suggesting that the use of VTM may indeed be beneficial during the study selection activity when performing an SLR.